scMET

DOI: 10.18129/B9.bioc.scMET    

This is the development version of scMET; for the stable release version, see scMET.

Bayesian modelling of cell-to-cell DNA methylation heterogeneity

Bioconductor version: Development (3.17)

High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression.

Author: Andreas C. Kapourani [aut, cre] , John Riddell [ctb]

Maintainer: Andreas C. Kapourani <kapouranis.andreas at gmail.com>

Citation (from within R, enter citation("scMET")):

Installation

To install this package, start R (version "4.3") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("scMET")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("scMET")

 

HTML R Script scMET analysis using synthetic data
PDF   Reference Manual
Text   NEWS

Details

biocViews Bayesian, Clustering, Coverage, DNAMethylation, DifferentialExpression, DifferentialMethylation, Epigenetics, FeatureExtraction, GeneExpression, GeneRegulation, Genetics, ImmunoOncology, Regression, Sequencing, SingleCell, Software
Version 1.1.0
In Bioconductor since BioC 3.16 (R-4.2) (< 6 months)
License GPL-3
Depends R (>= 4.2.0)
Imports methods, Rcpp (>= 1.0.0), RcppParallel (>= 5.0.1), rstan (>= 2.21.3), rstantools (>= 2.1.0), VGAM, data.table, MASS, logitnorm, ggplot2, matrixStats, assertthat, viridis, coda, BiocStyle, cowplot, stats, SummarizedExperiment, SingleCellExperiment, Matrix, dplyr, S4Vectors
LinkingTo BH (>= 1.66.0), Rcpp (>= 1.0.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.21.3), StanHeaders (>= 2.21.0.7)
Suggests testthat, knitr, rmarkdown
SystemRequirements GNU make
Enhances
URL
BugReports https://github.com/andreaskapou/scMET/issues
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package scMET_1.1.0.tar.gz
Windows Binary
macOS Binary (x86_64) scMET_1.1.0.tgz
macOS Binary (arm64)
Source Repository git clone https://git.bioconductor.org/packages/scMET
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/scMET
Package Short Url https://bioconductor.org/packages/scMET/
Package Downloads Report Download Stats

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